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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.22.2

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-03-24, 08:36 UTC based on data in: /mnt/lustre/RDS-live/downing/LSDV-PVG/work/0f/d291da34f7e2709261ed0dfc0edbaf

        ODGI

        ODGI is an optimized dynamic graph/genome implementation, for efficient analysis and manipulation of pangenome graphs structured in the variation graph model.

        Detailed ODGI stats table.

        Showing 2/2 rows and 10/14 columns.
        Sample NameLengthNodesEdgesPathsComponentsACTGN
        seqwish
        32768
        1681
        2251
        11
        1
        9774
        7065
        8471
        6901
        551
        smooth
        40305
        1286
        1722
        11
        1
        12399
        8614
        10502
        8682
        108

        Sum of path node distances

        For each path we iterate from node to node and count the node / nucleotide distance of nodes on the pangenome level normalized by the path length. If a node is reversed, we count the node distance twice.

        This value allows you to evaluate the sorting goodness - how linear the graph is.

        Created with MultiQC

        For each path we iterate from node to node and count the node / nucleotide distance mean_links_length of nodes within the same path only. We then normalized by the path length.

        This value allows you to evaluate the sorting goodness - how linear the graph is.

        MultiQC v1.22.2 - Written by Phil Ewels, available on GitHub.

        This report uses Plotly, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.